The use of cost-effectiveness analysis (CEA) as a tool for the economic evaluation of medical and public health interventions has increased dramatically over the last 20 years. Yet, despite the prevalence of these analyses, health care technological adoption patterns reveal large disparities in the employment of cost-effectiveness criteria. These disparities may reflect the fact that cost-effectiveness ratios as they are currently expressed do not include all the dimensions germane to making a welfare-improving decision. In particular, there are often large degrees of uncertainty about both the current costs and benefits of technology adoption and/or coverage. Furthermore, the influence of current technology adoption and coverage decisions on the use of future potential interventions is often overlooked. In this project, we will develop methodologies to assess the economic value of health interventions that can adequately and simply reflect these dimensions and empirically estimate their importance in clinical decision-making. The ultimate goal is to derive analytic tools that more accurately and systematically capture the decision-maker's preferences with regard to uncertainty. These new methods will result in measures that more appropriately measure welfare, thus providing a stronger basis for decision-making in the health care context.